High Efficiency Solar Membranes Structurally Designed with 3D Core–2D Shell SiO<sub>2</sub>@Amino-Carbon Hybrid Advanced Composite for Facile Steam Generation
Bibliographic record
Abstract
Steam generation through efficient utilization of solar energy is a promising technology in addressing the challenge of global freshwater shortage and water pollution. One of the biggest hurdles for traditional photothermal membranes to function continuously in a high temperature, high salt, and corrosive environment has been attributed to their rapid decline of mechanical properties. In this work, a highly efficient solar-driven interfacial water evaporation system has been developed via a polydopamine/carbon/silicon (PCS) composite membrane supported by a floating insulation foam substrate. A 3.1 fold increase in the water vaporization rate was recorded compared with the pure water system. The 2D-carbon nanolayer on the surface was successfully prepared by carbonizing low-cost linear polyethylene with a glass fiber (GF) membrane as the substrate, and then the carbon membrane was modified with dopamine to control water transport on the carbon coating and within the glass fiber. The PCS membrane has a high efficiency for solar steam generation owing to high optical absorption and has excellent solar thermal conversion capability. The evaporation rate and solar thermal conversion efficiency of the PCS membrane under simulated sunlight irradiation with 1 sun (1 kW·m –2 ) are 1.39 kg·m –2 ·h –1 and 80.4% respectively, which are significantly higher compared to GF membrane, carbon/silicon (CS) membrane, and pure water without a photothermal membrane. The water evaporation system retained high efficiency after 20 cycles under simulated sunlight irradiation of 1 sun. This study provides critical insight on the design and fabrication of a highly efficient and durable evaporation system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".